Lower margins are tied to companies’ climate performance rather than to low-carbon assets

Summary Lenders are likely to face significant financial risks from the shift to a low-carbon economy, but it remains unclear whether such risks are incorporated into their lending practices. The extent of this risk depends on whether banks incorporate such risks into their lending activity and whether financial instruments’ tenors are long enough to cover the period when such risks materialize. Using a case study of shipping loans, we combine quantitative data and semi-structured interviews with key shipping debt providers. Our results show that banks, in particular signatories of the Poseidon Principles, a voluntary disclosure initiative in shipping, have started to price in the climate score of shipowners they lend to after the Paris Agreement but on a corporate rather than an asset basis. However, signatories do not differentiate their margins based on a ship’s carbon intensity, despite a relatively long loan maturity, reinforcing the limitations of disclosure initiatives to influence investment outlays.


Sample bias
Table S2 shows the control variables used in our models in the various samples of estimation.On average, the loans provided are much larger in the corporate finance sample than when only financing ships.The average number of lenders is also lower in the ship samples than in the corporate finance samples.Not surprisingly, the large majority of the loans in the ships sample are secured by a collateral, which is likely to be secured by the financed ship directly.On the other hand, only a minority of corporate loans are secured.To check whether those samples are biased, we compare the average leverage, profitability and company size to all companies classified under the NAIC "Deep sea, coastal and Great Lakes water transportation", and to all companies which provided a ticker in Clarksons over the period 2010 to 2021.The results can be found in Figure S3.It is first worth noting that even those two samples might be biased compared to the average shipowner, as most shipowners do not report publicly that information.Second, those two samples do not compare well with each other, with companies classified under the NAIC "Deep sea, coastal and Great Lakes water transportation" being significantly small than those who reported in Clarksons which have a ticker.It can be expected that only the largest shipowners would report publicly their information and/or be publicly listed.
Borrowers of our corporate finance sample (sample 2) are much larger than the average company classified under the NAIC "Deep sea, coastal and Great Lakes water transportation" and larger than the average Clarksons company reporting a ticker.Borrowers who borrowed money to finance ships (samples 1) are on average much smaller and are a bit more leveraged than those of the corporate finance sample and of shipowners who have displayed a ticker.They are however larger than the average firm of NAIC "Deep sea, coastal and Great Lakes water transportation" and have similar leverage.This suggests that both samples, but in particular the corporate sample, are biased towards large firms in terms of assets.The variables are summarized before the logarithm transformations We further check the bias of the sample by comparing the average fleet size of the shipowner, expressed in number of ships, compared to the average fleet size in Clarksons.Figure S3 shows the results.It is worth noting that the fleet size corresponds to the current fleet size, not the fleet size at the time when the loan was emitted.This is because the Clarksons WFR does not provide exhaustive information on the ships which have been scrapped or sold in the past, especially in the beginning of the sample period, so the past fleet size could not be computed.
The figure clearly shows that our ship sample (sample 1) biased towards larger shipowners compared to the total sample of shipowners.This could have two main explanations.First, it is likely that larger shipowners have a disproportionately large access to the debt market to finance ships.This was confirmed during the interviews.
So although our samples are not representative of the shipowners in general, they might not be biased compared to the average shipping loan.A second explanation is that our samples are biased compared to shipping loans in general, as they only cover a small part of the total debt provided to shipowners.This might be the case because Dealscan only contains syndicated loans, which are often used to finance larger amount and might therefore only be available to larger shipowners.On the other hand, our corporate sample is not particularly biased against small shipowners -actually it is slightly biased towards them.

Robustness analysis
To check the robustness of the results, we conducted several robustness checks.
The results are in the supplementary materials.
We first recalculated the model using alternative measures of the corporate environment rating (Refinitiv environmental score, which equals to the Refinitiv environmental score -controversies score) and ship environmental rating (AER, energy saving technology installed) and by removing ship finance from the corporate sample (sample 4).Furthermore, to address potential influences on our primary findings, we conducted additional analyses by re-estimating equation (2) in the main text employing alternative sets of dummy variables.First, we incorporated dummy variables to account for the specific industry within which the companies operate, as classified under the "major industry group" as provided by Dealscan.Finally, to incorporate unobservable company characteristics that remain constant over time, we incorporated company dummies into both models during estimation.By doing so, the derived coefficients can be interpreted as specific effects within each company.
This latter sensitivity check could only be conducted on the corporate samples, as the ships' sample was too small and showed signs of overfitness.
A summary of the robustness analysis is provided in NA not measured/sample too small 0 non-significant coefficient 1 Positive 10% significant 2 Positive 5% significant 3 Positive 1% significant -1 Negative 10% significant -2 Negative 5% significant -3 Negative 1% significant Table S3: Summary of the robustness analysis a. Positive corresponds to a positive pricing of climate performance, i.e. a negative coefficient on the CDP/Refinitiv scores (a higher score leads to a lower margin) and a positive coefficient on the AER (a higher carbon intensity leads to a higher margin).Inversely, negative corresponds to a negative pricing of climate performance.
b.The colours correspond to the level of significance of the coefficient of interest.Expectations concerning future stranded assets 10.How do you feel the demand for cargo shipping such as oil, coal and natural gas will evolve in the coming [*insert tenor] years?

Deal
11. How likely do you feel it is that it will impact the value of the fleet you finance?
12. How do you feel the pressures to limit carbon emissions from shipping will evolve in the coming [*insert tenor] years?
13. How likely do you feel it is that the ships you finance lose their value because of efforts to limit carbon emissions from shipping?
14. How do you mitigate for those risks (if at all)?
15.Under which conditions would you finance alternative-fuelled ships?

Figure
Figure S1: Samples compositionThe labels correspond to the number of observations

Figure S3 :
Figure S3: Shipowner size by sample a.The first four columns represent the number of observations in the sample.b.The last column corresponds to the number of ships built between 2010 and 2021.c.The numbers plotted correspond to current fleet size of the shipowner rather than fleet size at the time of loan provision, as it was not possible from Clarksons data to build past fleet size by shipowner

Table S1 :
Results of the WALS procedure on the ship sample

Table S2 :
Average of continuous control variables inSample 1 and 2

Table S3Error !
Referencesource not found.. From this, it appears that the positive pricing of corporate climate performance after the Paris Agreement is robust across most model specifications when using CDP as a measure of climate performance (although the significance is lower in some model specifications).The results using Refinitiv environmental score points to the same direction, but the results are inconsistent between various model specifications, suggesting that the Refinitiv environmental score is not a robust variable.Similarly, the positive pricing of climate performance at the corporate level by Poseidon signatories is robust across model specifications (samples, industry and borrowers fixed effects) when measured by the CDP performance.The results are not robust however not using when using the Refinitiv If you had to give 3 main factors you consider when deciding whether or not you will provide finance for a ship, which ones would they be?5.If you had to give 3 main factors which influence the interest rate you give, what would they be?6.Why signing the Poseidon Principles?Evolution of the industry over the last decade 7. Could you tell me the story of the first ship investment that you made in your carrier?8. Could you tell me the story of the last ship investment that you made? 9. Have you observed any evolution in the way your company views and mitigates for climate risks since you joined?

Table S5 :
Detailed results by period.Central model

Table S7 :
Detailed results by period -Sensitivity: borrower ID fixed effect

Table S8 :
Detailed results with Paris Agreement dummy.Central model

Table S9 :
Detailed results with Paris Agreement dummy.Sensitivity: industry fixed effect

Table S10 :
Detailed results with Paris Agreement dummy.Sensitivity: borrower ID fixed effect

Table S11 :
Detailed results, effect of the Poseidon Principles.Central model.

Table S13 :
Detailed results, effect of the Poseidon Principles.Sensitivity: industry fixed effects

Table S14 :
Detailed results, effect of the Poseidon Principles.Sensitivity: borrower ID fixed effect